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Niessen, A. Susan M.; Meijer, Rob R.; Tendeiro, Jorge N. – Educational Measurement: Issues and Practice, 2019
A longstanding concern about admissions to higher education is the underprediction of female academic performance by admission test scores. One explanation for these findings is selection system bias, that is, not all relevant KSAOs that are related to academic performance and gender are included in the prediction model. One solution to this…
Descriptors: College Admission, High Stakes Tests, Gender Differences, Sampling
Bozpolat, Ebru – Educational Sciences: Theory and Practice, 2016
The purpose of this study was to reveal whether the low, medium, and high level self-regulated learning strategies of third year students at the Education Faculty of Cumhuriyet University can be predicted by the variables of gender, academic self-efficacy, and general academic average. The study uses the Relational Screening Model. The dependent…
Descriptors: Learning Strategies, Self Efficacy, Foreign Countries, Gender Differences
Danner, Fred W.; Toland, Michael D. – Journal of Early Adolescence, 2013
This study assessed how socioeconomic status (SES), race/ethnicity, and birth weight interacted to predict differential patterns of body mass index (BMI) growth among U.S. children born in the early 1990s. Three BMI growth trajectories emerged--one above the 50th percentile across the age range of 5 to 14, one in which children rapidly became…
Descriptors: Socioeconomic Status, Race, Ethnicity, Birth
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing

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